Many of the over 232,000 women who developed invasive breast cancer last year were unaware of disease risk factors, their personal risk, and available risk management strategies. Along with better-known risk factors such as family history, having dense breasts (density) is one of the strongest breast cancer risk factors. Almost half of US states now require disclosure of density status following routine screening mammography. Disclosure provides an opportunity to capture women who are at clinically elevated risk due to density and additional risk factors, such as family history, and to inform them of risk management options available to them. Guidelines have long recommended risk counseling for women with clinically elevated breast cancer risk, including discussion of chemoprevention and additional breast imaging such as MRI. While uptake is an individual, preference-based decision, population use of these risk management options is low and efforts to improve uptake are limited. Our pilot data suggest that patients have strong interest in density-focused risk counseling and that our pilot intervention can shift risk management intentions. These data, together with the confluence of mandatory density reporting, expanded risk management guidelines, and coverage of services under the ACA provides an unprecedented opportunity to expand breast cancer risk management and to assess the economic impact. We propose the first randomized controlled trial to evaluate a method of density disclosure. Our goal is to encourage uptake of guideline-informed risk management without increasing distress. Guided by Protection Motivation Theory, we will implement and evaluate a web-based interactive intervention vs. usual care among members of Group Health (aged 40-69) whose high breast density and other risk factors place them at high 5-year (>1.66 percent) or lifetime (>20 percent) risk for breast cancer. We will assess the mechanisms of intervention effect and conduct an economic evaluation alongside the trial. Patients will complete assessments at pre-randomization baseline, as well as 6 weeks and 12 months post-randomization. We will integrate these self-report data with healthcare utilization and cost data to assess the potential public health impact of our intervention. Our aims are to 1) assess intervention effects on uptake and distress, 2) identify mediators/moderators of intervention impact on uptake, and 3) extend the time horizon of the trial to estimate the lifetime costs, benefits, and harms of the intervention from different perspectives using a Cancer Intervention and Surveillance Modeling Network (CISNET) model. Our trial will move the field forward by informing the sweeping national policy integration requiring density disclosure and estimating the cost-effectiveness of our approach.